{"id":"W2463675895","doi":"","title":"Evaluation of the novel medical imaging software e-ASPECTS for patient selection in stroke","year":2015,"lang":"en","type":"article","venue":"Discovery Research Portal (University of Dundee)","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Stroke (engine); Selection (genetic algorithm); Medical physics; Software; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002835017,0.00007511234,0.000184443,0.0002487047,0.00006200929,0.000006710764,0.0002082539,0.00005977441,0.000114787],"category_scores_gemma":[0.001547087,0.00006798762,0.000106323,0.0003704337,0.000237595,0.0002646763,0.0002751726,0.0002448128,0.000001550807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002851825,"about_ca_system_score_gemma":0.001299412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000956603,"about_ca_topic_score_gemma":0.0009144178,"domain_scores_codex":[0.9963022,0.0001270377,0.0001613497,0.0002205791,0.002945649,0.0002431544],"domain_scores_gemma":[0.998513,0.0001040148,0.000114871,0.0001950044,0.0009584596,0.0001145906],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004393858,0.005327198,0.607723,0.001126769,0.001446642,0.0002006835,0.01370609,0.001469073,0.03599437,0.006472473,0.1604678,0.161672],"study_design_scores_gemma":[0.03165899,0.002012582,0.6546736,0.001683206,0.001086673,0.0001138998,0.1673421,0.117809,0.01514331,0.002447044,0.005450556,0.0005790611],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9791875,0.0001027199,0.005447272,0.001037377,0.00007438428,0.001055797,0.00004033511,0.00001041287,0.01304419],"genre_scores_gemma":[0.9987108,0.00001072069,0.0005568613,0.00002288634,0.00002623174,0.000003936708,0.00002377014,0.000008571066,0.000636238],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1610929,"threshold_uncertainty_score":0.2772455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07768916019900726,"score_gpt":0.3442399451702891,"score_spread":0.2665507849712818,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}